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Literacy Rate for Adult and Youth 📚
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---
title: "xxxxxxx"
output:
flexdashboard::flex_dashboard:
theme:
version: 4
bootswatch: sketchy
orientation: rows
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(naniar)
library(readxl)
library(tidyverse)
library(plotly)
library(ggmap)
library(gganimate)
library(gifski)
library(png)
# install.packages("bslib")
library(bslib)
```
Overview {data-icon="fa-globe"}
=============================
Inputs {.sidebar data-width=400}
-------------------------------------
### Table of Contents
Row{data-height=150}
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### XXXXX
Row{data-height=600}
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### Introduction
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```{r, include=FALSE}
excel_sheets("data/API_4_DS2_en_excel_v2_2252569.xls")
data_income <- read_excel("data/CLASS.xls", range = anchored("C5", dim = c(220,5))) %>%
select(-X) %>%
filter(Economy != "x")
```
```{r, include=FALSE}
df<- read_excel("Data/API_4_DS2_en_excel_v2_2252569.xls", sheet = 1, range = "A4:BM42772") %>%
select("Country Name",
"Country Code",
"Indicator Name",
"2000":"2019") %>%
rename("country_name" = "Country Name",
"indicator" = "Indicator Name",
"country_code" = "Country Code")
```
```{r, include=FALSE}
df %>%
group_by(country_name) %>%
miss_var_summary() %>%
arrange(-pct_miss)
```
```{r}
df_filter <- df %>%
filter(!(country_name %in% c("Faroe Islands",
"Greenland",
"Isle of Man",
"Not classified",
"St. Martin (French part)",
"Northern Mariana Islands",
"Kosovo",
"American Samoa",
"Gibraltar",
"Sint Maarten (Dutch part)",
"Turks and Caicos Islands",
"Channel Islands",
"Guam",
"French Polynesia",
"Curacao",
"New Caledonia",
"Virgin Islands (U.S.)",
"Nauru",
"Palau",
"Somalia",
"Guinea-Bissau")))
```
```{r, include=FALSE}
data_joined <- df_filter %>%
left_join(data_income, by = c("country_name" = "Economy",
"country_code" = "Code"))
```
Literacy Rate {data-icon="ion-android-search"}
=============================
Row{data-height=150}
--------------------------------------
### Literacy Rate for Adult and Youth
```{r rw}
valueBox(value = "Literacy Rate for Adult and Youth 📚", icon = "ion-university", caption = "GLobal - Reginal - Income Levels", color = "skyblue")
```
Row{data-height=500}
----------------------------------
### Global Literacy Rate
```{r longdata-my}
# filter and pivot data to longer form
lit_long <- data_joined %>%
filter(str_detect(indicator, "Literacy")) %>%
filter(str_detect(indicator, "total")) %>%
pivot_longer(cols = c("2000":"2019"),
names_to = "year",
values_to = "rate")
# remove strings in indicator variable
lit_long$indicator <- lit_long$indicator %>%
substring(16) %>%
str_remove("\\(") %>%
str_remove("\\)") %>%
str_remove("\\%") %>%
str_remove("total of people ")
```
```{r plot1my, eval = FALSE, catche = TRUE}
# animation
plot1 <- lit_long %>%
filter(country_name == "World") %>%
ggplot(aes(x = year,
y = rate,
fill = indicator)) +
geom_col(position = "dodge", width = 0.7) +
theme_light() +
ylim(0, 100) +
theme(axis.text.x = element_text(angle = 60, vjust = 0.5, hjust=0.5)) +
scale_fill_brewer(palette = "Paired") +
labs(x = "Year",
y = "Literacy Rate",
fill = "Age") +
ggtitle("Global Literacy Rate for Adult and Youth between 2000-2019") +
transition_time(as.numeric(year)) +
enter_grow() +
shadow_mark(past = TRUE)
animate(plot1, fps = 20, duration = 15, end_pause = 80)
```
Row{data-height=600}
----------------------------------
### Regional Literacy Rate
```{r plot2my, eval = FALSE, catche = TRUE}
# animation
plot2 <- lit_long %>%
filter(country_name %in% c("Central Europe and the Baltics",
"East Asia & Pacific",
"Europe & Central Asia",
"Latin America & Caribbean",
"Middle East & North Africa",
"South Asia",
"Sub-Saharan Africa")) %>%
group_by(indicator) %>%
ggplot(aes(x = year,
y = rate,
group = country_name,
color = country_name)) +
geom_line() +
facet_wrap(~indicator) +
theme_light() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=0.5)) +
scale_colour_brewer(palette = "Paired") +
labs(x = "Year",
y = "Literacy Rate",
color = "Regions") +
ggtitle("Literacy Rate for Adult and Youth in Differenet Regions") +
transition_reveal(as.numeric(year)) +
enter_grow() +
shadow_mark(past = TRUE)
animate(plot2, fps = 20, duration = 8, end_pause = 80)
```
### Literacy Rate for Different Income Groups
```{r plot3my, eval = FALSE, catche = TRUE}
# animation
plot3 <- lit_long %>%
filter(country_name %in% c("Low income",
"Lower middle income",
"Middle income",
"Upper middle income")) %>%
ggplot(aes(x = year,
y = rate,
fill = indicator)) +
geom_col(position = "dodge") +
facet_wrap(~country_name,
ncol = 2) +
theme_light() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=0.5)) +
scale_fill_brewer(palette = "Paired") +
labs(x = "Year",
y = "Literacy Rate",
fill = "Age") +
ggtitle("Literacy Rate for Adult and Youth in Differenet Income Groups") +
transition_time(as.numeric(year)) +
enter_grow() +
shadow_mark(past = TRUE)
animate(plot3, fps = 20, duration = 20, end_pause = 80)
```
Section 2 {data-icon="ion-android-search"}
=============================
Row{data-height=150}
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### ???
```{r}
valueBox(value = 2,icon = "ion-university",caption = "???", color = "orange")
```
Row
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Section 3 {data-icon="ion-android-search"}
=============================
Row{data-height=150}
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### ???
```{r}
valueBox(value = 2,icon = "ion-university",caption = "???", color = "coral")
```
Row
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### Number of Offences by Gender
```{r}
```
Section 4 {data-icon="ion-android-search"}
=============================
Row{data-height=150}
--------------------------------------
### ???
```{r}
valueBox(value = 2,icon = "ion-university",caption = "???", color = "coral")
```
Row
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### Number of Offences by Gender
```{r}
```